{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年1月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in First-Tier Cities 程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../py')\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and city in ('北京','上海','广州','深圳')  and monthly_salary<80000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']\n",
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>zhinengleibie</th>\n",
       "      <th>career</th>\n",
       "      <th>real_province</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>company_description</th>\n",
       "      <th>...</th>\n",
       "      <th>pl_visual_basic</th>\n",
       "      <th>publish_date</th>\n",
       "      <th>published_on_weekend</th>\n",
       "      <th>tag_baby_care</th>\n",
       "      <th>tag_five_insurance</th>\n",
       "      <th>tag_flexible</th>\n",
       "      <th>tag_no_overtime</th>\n",
       "      <th>tag_rest_one_day</th>\n",
       "      <th>tag_rest_two_days</th>\n",
       "      <th>tag_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>27199</td>\n",
       "      <td>118720034</td>\n",
       "      <td>17500</td>\n",
       "      <td>222</td>\n",
       "      <td>高级后台开发工程师/Java开发工程师（北京）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>营销实验室Convertlab（上海欣兆阳信息科技有限公司）为企业提供创新的数字营销平台DM...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-25</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18898</td>\n",
       "      <td>116813374</td>\n",
       "      <td>12916</td>\n",
       "      <td>200</td>\n",
       "      <td>Java开发工程师（2020届）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>算法工程师</td>\n",
       "      <td>None</td>\n",
       "      <td>广东</td>\n",
       "      <td>广州</td>\n",
       "      <td>广州汇智通信技术有限公司是专业从事国家特殊通信系统研制工作的大型国有控股混合所有制企业。公司...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18571</td>\n",
       "      <td>116717167</td>\n",
       "      <td>10000</td>\n",
       "      <td>200</td>\n",
       "      <td>海外业务软件工程师（2020届）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海海隆软件有限公司成立于1989年4月，是国内著名软件企业、国家规划布局内重点软件企业。公...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12578</td>\n",
       "      <td>114568087</td>\n",
       "      <td>3750</td>\n",
       "      <td>151</td>\n",
       "      <td>C#软件开发工程师（雄安新区岗位）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京其然科技有限公司，名字源于，顺 其其自 然然，主旨顺其自然、水到渠成。北京其然科技有限公...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4054</td>\n",
       "      <td>108834444</td>\n",
       "      <td>15000</td>\n",
       "      <td>150</td>\n",
       "      <td>Java开发工程师</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_id  monthly_salary  headcount                    title  \\\n",
       "27199  118720034           17500        222  高级后台开发工程师/Java开发工程师（北京）   \n",
       "18898  116813374           12916        200         Java开发工程师（2020届）   \n",
       "18571  116717167           10000        200         海外业务软件工程师（2020届）   \n",
       "12578  114568087            3750        151        C#软件开发工程师（雄安新区岗位）   \n",
       "4054   108834444           15000        150                Java开发工程师   \n",
       "\n",
       "      zhinengleibie career real_province province city  \\\n",
       "27199         软件工程师  一般程序员          None       北京   北京   \n",
       "18898         软件工程师  算法工程师          None       广东   广州   \n",
       "18571         软件工程师  一般程序员          None       上海   上海   \n",
       "12578         软件工程师  一般程序员          None       北京   北京   \n",
       "4054          软件工程师  一般程序员          None       北京   北京   \n",
       "\n",
       "                                     company_description  ... pl_visual_basic  \\\n",
       "27199  营销实验室Convertlab（上海欣兆阳信息科技有限公司）为企业提供创新的数字营销平台DM...  ...           False   \n",
       "18898  广州汇智通信技术有限公司是专业从事国家特殊通信系统研制工作的大型国有控股混合所有制企业。公司...  ...           False   \n",
       "18571  上海海隆软件有限公司成立于1989年4月，是国内著名软件企业、国家规划布局内重点软件企业。公...  ...           False   \n",
       "12578  北京其然科技有限公司，名字源于，顺 其其自 然然，主旨顺其自然、水到渠成。北京其然科技有限公...  ...           False   \n",
       "4054   大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...  ...           False   \n",
       "\n",
       "      publish_date published_on_weekend tag_baby_care  tag_five_insurance  \\\n",
       "27199   2019-12-25                False         False                True   \n",
       "18898   2019-12-03                False         False                True   \n",
       "18571   2019-01-02                False         False                True   \n",
       "12578   2019-12-26                False         False               False   \n",
       "4054    2019-01-02                False         False                True   \n",
       "\n",
       "       tag_flexible  tag_no_overtime  tag_rest_one_day  tag_rest_two_days  \\\n",
       "27199          True            False             False              False   \n",
       "18898         False            False             False              False   \n",
       "18571          True            False             False              False   \n",
       "12578         False            False             False              False   \n",
       "4054          False            False             False              False   \n",
       "\n",
       "       tag_stock  \n",
       "27199       True  \n",
       "18898      False  \n",
       "18571      False  \n",
       "12578      False  \n",
       "4054       False  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38115, 94)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120827"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5000., 11500., 14583., 20000., 37500.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年1月中国一线城市程序员的平均工资为16285元，工资中位数为14583元，其中95%的人的工资位于5000到37500元之间。\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月中国一线城市程序员的平均工资为{salary_mean:.0f}元，工资中位数为{q[2]:.0f}元，其中95%的人的工资位于{q[0]:.0f}到{q[4]:.0f}元之间。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2020, Developers in China earn 16285 Yuan as average, the median is 14583 Yuan, 95% of them earn between 5000 and 37500 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print(f'In {year}, Developers in China earn {salary_mean:.0f} Yuan as average, '\n",
    "      f'the median is {q[2]:.0f} Yuan, 95% of them earn between {q[0]:.0f} and {q[4]:.0f} Yuan.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2560671f648>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=11572.799483675622, pvalue=0.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<40000].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(2000, 100), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Functions\n",
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "    \n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >career</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow0_col0\" class=\"data row0 col0\" >系统架构师</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow0_col1\" class=\"data row0 col1\" >25364</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow0_col2\" class=\"data row0 col2\" >6000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow0_col3\" class=\"data row0 col3\" >23000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow0_col5\" class=\"data row0 col5\" >5061</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow0_col6\" class=\"data row0 col6\" >4.19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow1_col0\" class=\"data row1 col0\" >算法工程师</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow1_col1\" class=\"data row1 col1\" >21182</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow1_col2\" class=\"data row1 col2\" >5500</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow1_col5\" class=\"data row1 col5\" >13018</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow1_col6\" class=\"data row1 col6\" >10.77%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >0</th>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow2_col0\" class=\"data row2 col0\" >一般程序员</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow2_col1\" class=\"data row2 col1\" >15223</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow2_col2\" class=\"data row2 col2\" >5000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow2_col3\" class=\"data row2 col3\" >13500</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow2_col4\" class=\"data row2 col4\" >35000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow2_col5\" class=\"data row2 col5\" >102550</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow2_col6\" class=\"data row2 col6\" >84.87%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow3_col0\" class=\"data row3 col0\" >爬虫工程师</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow3_col1\" class=\"data row3 col1\" >12567</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow3_col2\" class=\"data row3 col2\" >2764.29</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow3_col4\" class=\"data row3 col4\" >26583.3</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow3_col5\" class=\"data row3 col5\" >171</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow3_col6\" class=\"data row3 col6\" >0.14%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow4_col0\" class=\"data row4 col0\" >生物信息工程师</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow4_col1\" class=\"data row4 col1\" >11389</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow4_col4\" class=\"data row4 col4\" >15000</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow4_col5\" class=\"data row4 col5\" >27</td>\n",
       "                        <td id=\"T_4af15578_2f5c_11ea_bfd3_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.02%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25603b2b608>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16285.028040090378"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120827"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    38115.000000\n",
       "mean         3.170064\n",
       "std          3.912043\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          5.000000\n",
       "max        222.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120827"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "headcount_by_cities = data.groupby(by='city').headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "上海    39614\n",
       "北京    22805\n",
       "广州    25081\n",
       "深圳    33327\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headcount_by_cities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(headcount_by_cities.values, labels=headcount_by_cities.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >city</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow0_col0\" class=\"data row0 col0\" >北京</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow0_col1\" class=\"data row0 col1\" >18048</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow0_col2\" class=\"data row0 col2\" >3750</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow0_col3\" class=\"data row0 col3\" >15000</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow0_col4\" class=\"data row0 col4\" >45000</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow0_col5\" class=\"data row0 col5\" >22805</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow0_col6\" class=\"data row0 col6\" >18.87%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow1_col0\" class=\"data row1 col0\" >上海</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow1_col1\" class=\"data row1 col1\" >16653</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow1_col2\" class=\"data row1 col2\" >5250</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow1_col3\" class=\"data row1 col3\" >15000</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow1_col4\" class=\"data row1 col4\" >37500</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow1_col5\" class=\"data row1 col5\" >39614</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow1_col6\" class=\"data row1 col6\" >32.79%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow2_col0\" class=\"data row2 col0\" >深圳</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow2_col1\" class=\"data row2 col1\" >16488</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow2_col2\" class=\"data row2 col2\" >6000</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow2_col5\" class=\"data row2 col5\" >33327</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow2_col6\" class=\"data row2 col6\" >27.58%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow3_col0\" class=\"data row3 col0\" >广州</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow3_col1\" class=\"data row3 col1\" >13831</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow3_col2\" class=\"data row3 col2\" >3750</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow3_col4\" class=\"data row3 col4\" >30000</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow3_col5\" class=\"data row3 col5\" >25081</td>\n",
       "                        <td id=\"T_4b233ae6_2f5c_11ea_8e06_701ce71031efrow3_col6\" class=\"data row3 col6\" >20.76%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25602693a48>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_col(data,'city')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_beijing=data[data.city=='北京'].monthly_salary\n",
    "salary_shanghai=data[data.city=='上海'].monthly_salary\n",
    "salary_guangzhou=data[data.city=='广州'].monthly_salary\n",
    "salary_shenzhen=data[data.city=='深圳'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=93.24902474821212, pvalue=5.184770014585789e-22)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_beijing, salary_shanghai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=38.17288463174944, pvalue=6.578694915859506e-10)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shanghai, salary_shenzhen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=583.4919444081208, pvalue=5.066099401116921e-127)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shenzhen, salary_guangzhou)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow0_col1\" class=\"data row0 col1\" >rust</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow0_col2\" class=\"data row0 col2\" >25914</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow0_col3\" class=\"data row0 col3\" >27250</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow0_col4\" class=\"data row0 col4\" >9000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow0_col5\" class=\"data row0 col5\" >50521</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow0_col6\" class=\"data row0 col6\" >250</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow1_col1\" class=\"data row1 col1\" >python</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow1_col2\" class=\"data row1 col2\" >20639</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow1_col4\" class=\"data row1 col4\" >5500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow1_col5\" class=\"data row1 col5\" >45000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow1_col6\" class=\"data row1 col6\" >15814</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow1_col7\" class=\"data row1 col7\" >9.35%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow2_col1\" class=\"data row2 col1\" >go</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow2_col2\" class=\"data row2 col2\" >20324</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow2_col4\" class=\"data row2 col4\" >7000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow2_col5\" class=\"data row2 col5\" >45000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow2_col6\" class=\"data row2 col6\" >15279</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow2_col7\" class=\"data row2 col7\" >9.04%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow3_col1\" class=\"data row3 col1\" >lua</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow3_col2\" class=\"data row3 col2\" >20181</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow3_col3\" class=\"data row3 col3\" >19000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow3_col4\" class=\"data row3 col4\" >6460</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow3_col5\" class=\"data row3 col5\" >40000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow3_col6\" class=\"data row3 col6\" >1633</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow3_col7\" class=\"data row3 col7\" >0.97%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow4_col1\" class=\"data row4 col1\" >matlab</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow4_col2\" class=\"data row4 col2\" >19706</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow4_col4\" class=\"data row4 col4\" >5271</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow4_col5\" class=\"data row4 col5\" >40000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow4_col6\" class=\"data row4 col6\" >2651</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow4_col7\" class=\"data row4 col7\" >1.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow5_col1\" class=\"data row5 col1\" >typescript</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow5_col2\" class=\"data row5 col2\" >19226</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow5_col3\" class=\"data row5 col3\" >22500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow5_col4\" class=\"data row5 col4\" >6500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow5_col5\" class=\"data row5 col5\" >35000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow5_col6\" class=\"data row5 col6\" >882</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow6_col1\" class=\"data row6 col1\" >haskell</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow6_col2\" class=\"data row6 col2\" >19000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow6_col3\" class=\"data row6 col3\" >18929</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow6_col4\" class=\"data row6 col4\" >11500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow6_col5\" class=\"data row6 col5\" >27000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow6_col6\" class=\"data row6 col6\" >45</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow6_col7\" class=\"data row6 col7\" >0.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow7_col1\" class=\"data row7 col1\" >cpp</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow7_col2\" class=\"data row7 col2\" >18483</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow7_col3\" class=\"data row7 col3\" >16500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow7_col4\" class=\"data row7 col4\" >5250</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow7_col5\" class=\"data row7 col5\" >40000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow7_col6\" class=\"data row7 col6\" >29077</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow7_col7\" class=\"data row7 col7\" >17.20%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow8_col1\" class=\"data row8 col1\" >kotlin</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow8_col2\" class=\"data row8 col2\" >18312</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow8_col3\" class=\"data row8 col3\" >17500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow8_col4\" class=\"data row8 col4\" >7000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow8_col5\" class=\"data row8 col5\" >35000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow8_col6\" class=\"data row8 col6\" >572</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow9_col1\" class=\"data row9 col1\" >ruby</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow9_col2\" class=\"data row9 col2\" >18118</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow9_col3\" class=\"data row9 col3\" >17500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow9_col4\" class=\"data row9 col4\" >3848</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow9_col5\" class=\"data row9 col5\" >37391</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow9_col6\" class=\"data row9 col6\" >547</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow10_col1\" class=\"data row10 col1\" >perl</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow10_col2\" class=\"data row10 col2\" >18028</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow10_col3\" class=\"data row10 col3\" >16500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow10_col4\" class=\"data row10 col4\" >4890</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow10_col5\" class=\"data row10 col5\" >40000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow10_col6\" class=\"data row10 col6\" >1256</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.74%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow11_col1\" class=\"data row11 col1\" >julia</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow11_col2\" class=\"data row11 col2\" >17308</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow11_col3\" class=\"data row11 col3\" >12500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow11_col4\" class=\"data row11 col4\" >12500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow11_col5\" class=\"data row11 col5\" >25000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow11_col6\" class=\"data row11 col6\" >13</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow12_col1\" class=\"data row12 col1\" >swift</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow12_col2\" class=\"data row12 col2\" >16932</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow12_col3\" class=\"data row12 col3\" >15500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow12_col4\" class=\"data row12 col4\" >6114</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow12_col5\" class=\"data row12 col5\" >35958</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow12_col6\" class=\"data row12 col6\" >1391</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow13_col1\" class=\"data row13 col1\" >objective_c</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow13_col2\" class=\"data row13 col2\" >16096</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow13_col3\" class=\"data row13 col3\" >13667</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow13_col4\" class=\"data row13 col4\" >4711</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow13_col5\" class=\"data row13 col5\" >43812</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow13_col6\" class=\"data row13 col6\" >159</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow14_col1\" class=\"data row14 col1\" >java</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow14_col2\" class=\"data row14 col2\" >16084</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow14_col3\" class=\"data row14 col3\" >14500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow14_col4\" class=\"data row14 col4\" >5095</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow14_col5\" class=\"data row14 col5\" >37500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow14_col6\" class=\"data row14 col6\" >53856</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow14_col7\" class=\"data row14 col7\" >31.85%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow15_col1\" class=\"data row15 col1\" >php</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow15_col2\" class=\"data row15 col2\" >15767</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow15_col3\" class=\"data row15 col3\" >12999</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow15_col4\" class=\"data row15 col4\" >4500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow15_col5\" class=\"data row15 col5\" >37500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow15_col6\" class=\"data row15 col6\" >7267</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow15_col7\" class=\"data row15 col7\" >4.30%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow16_col1\" class=\"data row16 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow16_col2\" class=\"data row16 col2\" >14367</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow16_col3\" class=\"data row16 col3\" >14792</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow16_col4\" class=\"data row16 col4\" >6250</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow16_col5\" class=\"data row16 col5\" >22500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow16_col6\" class=\"data row16 col6\" >20</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow16_col7\" class=\"data row16 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow17_col1\" class=\"data row17 col1\" >javascript</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow17_col2\" class=\"data row17 col2\" >13982</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow17_col3\" class=\"data row17 col3\" >12500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow17_col5\" class=\"data row17 col5\" >29166</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow17_col6\" class=\"data row17 col6\" >19215</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow17_col7\" class=\"data row17 col7\" >11.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow18_col1\" class=\"data row18 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow18_col2\" class=\"data row18 col2\" >13408</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow18_col3\" class=\"data row18 col3\" >12500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow18_col4\" class=\"data row18 col4\" >4000</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow18_col5\" class=\"data row18 col5\" >27500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow18_col6\" class=\"data row18 col6\" >18464</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow18_col7\" class=\"data row18 col7\" >10.92%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow19_col1\" class=\"data row19 col1\" >delphi</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow19_col2\" class=\"data row19 col2\" >12476</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow19_col3\" class=\"data row19 col3\" >12500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow19_col4\" class=\"data row19 col4\" >4967</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow19_col5\" class=\"data row19 col5\" >22657</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow19_col6\" class=\"data row19 col6\" >416</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow20_col1\" class=\"data row20 col1\" >vba</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow20_col2\" class=\"data row20 col2\" >12401</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow20_col3\" class=\"data row20 col3\" >11500</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow20_col4\" class=\"data row20 col4\" >5768</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow20_col5\" class=\"data row20 col5\" >26292</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow20_col6\" class=\"data row20 col6\" >278</td>\n",
       "                        <td id=\"T_4b3fc9c8_2f5c_11ea_b2fa_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.16%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2560ccbcb48>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，haskell才是最赚钱的编程语言。python是主流语言里面最赚钱的，比java的工资多了3000元！vb是最不赚钱的了。其中，最赚钱的编程语言和最不赚钱的，工资居然差了2倍。所以，要选好编程语言呀！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_counts=data[~(data.edu=='')].groupby(by=['edu']).headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu\n",
       "初中及以下       15\n",
       "博士         268\n",
       "大专       30246\n",
       "本科       68184\n",
       "硕士        4233\n",
       "高中         219\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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f64GTXIeQwkhaxk/fYF9ynUOkDwYBP9Vp8UqXSvYYBZ4/HZ0fNnYuXpTT8bJSbuYDH3UdQrqnkj12P0anr4ud05ZbzRCXcvSNdENauztKkEr2GASefzVwoescUnh17cwask9rGUvZGYgWqShJKtk+Cjx/DPAt1zkkGgYSr11sl7nOIXIMLkw3pK90HUIOpZLtu5uAYa5DSHTOb8xptriUq2+lG9IDXYeQg1SyfRB4/qXA37vOIdEa28SsZNZ2uM4hcgwmADe6DiEHqWR7KfD8OnSO2IpgYPDcl7X6U7F07u1k74t76WzufPVtuzuxndZBqrL2yXRD+gTXISSkku29TwGTXIeQ4ljwnN3jOkM5ybZkWf3t1az65irW3LSG9k3trP7OalZ+bSWb7jzyUVHZfVnWfG8NLStbWPWNVXTu6WTHH3aw4ksryLXnaH6xGZPSIaB9VE24W0tKgEq2F/KTnT7tOocUj7fOHu86QznZ9X+7GLlgJFP+aQpVQ6pY+dWVjL5sNFP/ZSodTR3sDfZ2+7i2dW2Mu3Icoy8bTf3selrXtNK2to1hrxlGy8oWEtV6izpGC9IN6Te7DiEq2d76EqATJVeQVI7jJm21q1znKBcjXjuC+tnh/yKdzZ3k2nPUHh8eRp4alCLXmuv2cQO9gdRNr2Pfsn20rmylbnod1lps1rJ3yV4GzRlUtN8hhr6bbkjrWH7HVLI9CDzfBz7gOocU38WLcmtdZyg3LctbyO7LMuqyUWz7zTb2PL+HvS/uZeCsI094tday+6ndJAcmMUnDoNmDaF7cTNWwKtZ8f80RR8HSo+OBz7oOUelUsj37BjoBQEU6c5kd4jpDOenc28nG2zYy4f0Tws2/c+pperyJofOGkqw98v9CxhjGv2c8tRNraX6+mSFnDmH0m0eTrEsy6KRB7HlWu8f74Z/SDelxrkNUMpXsUQSefz5wqesc4sagVmbXtdndrnOUg1xnjnU/WsfYt42lemQ1ALWTa+nY0cHIBSOP+Lht92+j6S9NQDh5KlEXviW1b2mnenR1OOlJk4v7ow4d0uOUSvYIAs83aGWnimYgdcHf7FLXOcpB0+NNtK5pZeu9W1n5byvZ/dRutj+4nRELRpCoCd9m2ja0seXXWw553PALhrPrr7tY+bWVYKF+dj3Z1ixVQ6qoGV9D05+ajrqpWXrlA1rX2B1jrT4mdifw/HcAd7jOIW6tHclfrr8mNc91DpF+uqNxYeNVrkNUIo1kuxF4fg3wNdc5xL2J2/GNtd1PjRUpH+9IN6TTrkNUIpVs9z5MODNPKlwChs9ZZZe4ziHST4bwUEQpMpXsYQLPrwKud51DSsfFi+xO1xlECuDN6Yb0ya5DVBqV7Ku9C5joOoSUjtmr7XjXGUQKQKNZB1SyXQSen0DLJ8phajqZMabJrnedQ6QALks3pE9zHaKSqGQP9WbAcx1CSs/rn8utdJ1BpEA+4zpAJVHJHkr/8Um35i21da4ziBTIW9IN6eNdh6gUKtm8wPPnA2e4ziGladhe0jX77T7XOUQKIAl83HWISqGSPUgLacsRGaiZt1SH8khsvD/dkNba3EWgkgUCzz8VuMh1DiltFz2fa3edQaRABgHXuA5RCVSyIY1ipUfHb2EGWodU4uO6dEM65TpE3FV8yQaePw14q+scUvqSlrHeepa5ziFSIJOBt7kOEXcVX7KEm0z0d5BeufjZ3Jae7yVSNv7RdYC4q+hyCTw/BbzXdQ4pH6estKNcZxApoNPTDenzXIeIs4ouWcITso9xHULKR+1+/GHNdqvrHCIF9FHXAeKs0ktWs+ukTwyYi57PveQ6h0gBvTndkB7qOkRcVWzJBp4/CVjgOoeUn9e8aKtcZxApoFrgHa5DxFXFlixwNZX9+8sxGrWbE1OdVsfMSpy8z3WAuKrIksmfbedq1zmkPBmoP+Ml+6LrHCIFdHq6IX2i6xBxVJElC7ye8BgxkWOy4Lmc1jGWuNFoNgKVWrIfcB1AytuMDUxxnUGkwN6lFaAKr+JKNvD80cBlrnNIeUvlmDRls13hOodIAY0B3uA6RNxUXMkCfw9odqj028WLcutdZxApMG0yLrBKLFmtUywFcfpLdpjrDCIF9sZ0Q3q46xBxUlElG3j+SEBLiElBDGzjxPoW2+Q6h0gBVRGuhCcFUlElS7gvNuk6hMSDgeT8v9nAdQ6RAnuz6wBxUmkl+xbXASReLnwh5zqCSKEtSDek61yHiIuKKdnA8wcBr3OdQ+Jl/E5mJXK203UOkQIaQLiWgBRAxZQs8EagxnUIiRcDQ09ZYZe4ziFSYH/nOkBcVFLJalOxRGLBIrvLdQaRAnuTFqYojIoo2cDza4FLXOeQeJq11k5ynUGkwIYDr3EdIg4qomSBi4B61yEknqqzTB2/w65xnUOkwDTLuAAqpWS1f0EitWBRTiUrcaOSLYBKKdmLXQeQeDs7sNpSInEzKd2QnuU6RLmLfckGnu8D41znkHgb0sLsAe222XUOkQK70HWAchf7kgVe6zqAxJ+B6vNe1KE8Ejsq2X5SyYoUyOsW57QohcTN+emGtHEdopzFumQDz08A57vOIZVh8jZmYq11nUOkgEYCs12HKGexLlngZECnI5OiSFhGnbjGLnWdQ6TAtMm4H+JesjqYWorq4kV2u+sMIgV2gesA5SzuJatzx0pRnbTKjnWdQaTAzk83pOPeFZGJ+x/uXNcBpLLUdjBzxG67yXUOkQIaDsxxHaJcxbZkA88/ARjtOodUngXP5Za7ziBSYBe4DlCuYluyaFOxOHLuElvrOoNIgZ3mOkC5inPJznUdQCrTiGZmV3fYVtc5RAroVNcBylWcS1b7EMQJAwPOytgXXecQKaCZ6Yb0ANchylEsSzbwfAOkXeeQyvX653JtrjOIFFASDVyOSSxLFpgCDHIdQirXtE1Mc51BpMBOcR2gHMW1ZPWJS5xKWsZP32Bfcp1DpIBUssdAJSsSkYsX5XS8rMSJJj8dg7iW7EmuA4icttyOcJ1BpIDS6YZ0ynWIchPXktVIVpyra2fWkH1ay1hiowbwXYcoN7Er2cDzBwJTXecQMZCYv9hmXOcQKSBtJeyj2JUs4bkP4/h7SRm6oDGXdJ1BpIA0a76P4lhG2lQsJWNsE7OSWdvhOodIgahk+yiOJXuC6wAiBxgYctpyu8R1DpEC0a64PopjyU5yHUCkqwWL7G7XGUQKRCXbRypZkYh56+1xrjOIFMhYrWHcNypZkYhVZTl+4ja72nUOkQIwhMvWSi/FqmQDz08C413nEDncxYtya1xnECkQbTLug1iVLGHB6pAJKTlnZewQ1xlECkQzjPsgbiWrTcVSkga1cmJdmyZASSxoJNsHcSvZya4DiHTHQNX5jXap6xwiBaD32T6IW8lqJCsl67Uv5HKuM4gUwEjXAcqJSlakSCZuwzPWqmil3Klk+0AlK1IkCRiRXqVNxlL2dArHPohbyY51HUDkaC5eZHe4ziDST8PTDWnjOkS5iFvJDnIdQORo0qutjuOWcpcEdEhaL6lkRYqoppMZo3fZDa5ziPSTNhn3UtxKtt51AJGevH5RboXrDCL9pMlPvRS3ktVIVkreuUttnesMIv2kkWwvxaZkA8+vBqpc5xDpybC9zK7usC2uc4j0g0q2l2JTsmgUK2XCQO28pfZF1zlE+mGY6wDlQiUr4sBFz+faXWcQ6Yca1wHKRZxKVpOepGxM2cwM1xlE+iHykjXGxKKfUq4DFJBGslI2kpaxM9fZzLJJxnOdRY6uc28nbavbqD2ultSgQ98yO3d3khyYxKQqbm2GgpWsMeaLwO+BFUA18DbC+TVVxpg/A09ba1u73P9zwC5r7Q+7ea4qIGtLaPnSWHxSyFPJSlm5eFFui+sMlW79f61nxVdWsPW3W7u9Pbsvy5rvraFlZQurvrGKzj2d7PjDDlZ8aQW59hy7n67YsxcWeiT7HeCNwIeB+cBrCLdOvgGYeNh9zwXOPMLzfAR40Bizwxjzu/zXU8aYp7u7szHmM8aY2V2uf8wYc1L++2S+tLtljKkyxvR4/vI4jWQHuA4g0henrLCjXGeoZLuf3c2+ZftIDUqxZ9EehpwxhJqxh3ZH27o2kgOTNL/QTGJAgtY1rez6v1107ulk37J9tK5tPcKzF5i1nUAn0Gm6XBrI5i9zCWuzBrIJyCYs2STWJiCXtGQT2FzSkkthbdJik9hcymJTWJuy2JS1VJG/tNZWgamy1lZZa6qspcpCFdZUh9fNsGyuuRC/ljFmKjABeCR/+T6gjvD9/FSgFvjpYQ/L5v8W3fyZ7E3ATcaYh6y1F+df4wvAy4e97g+AbwD3AXcYYy6w1jYBFwNP5u/2BuBGY8z+/PVZwHLgwPVq4J/z2Y8oTiVbMpsHRHpjwH78oXvttl31RmV7rA6WT4cJ33w78sXTtXw6E5Azh5ZPdt3vt0+lJVuXrE60dzR3VuUe3b55+ltHrU92KZ+XVjSP2rSiZVptfbJ9z7aOgXPb2xfft7FttsmRSD2weeek2mTne/Y1Lz9QPtUWU2VtourAdUjkryfD+5BMYRPV1iZTlmSVtckqDn6fwqaqLKmktakUpFL5Sw5+lYq/Feh5zgCuIPy32w+sBGYABvhr/varjTEf42C51QHGGPN2Dpbt+6y1vya8IUm49OOBzccLgH897HUnAylr7RJjzL8DnwI+B4wGVgNYa+8F7j3wAGPMfcCHrbXr+/ILltI/Wn91+8lGpFQZMBc9n1v2q/OShS/ZsHw6yI92OFg+neaVwgnLJz/yySWwuQRkk5ZcMhz52GSXEdDhI58U1laF35tqa20qHP2QH/mYamup4tDyyZdRIhwVkUy98jOSVdhElbWplCVxoHyqwjJKpbDJA+VThU0lLVWp8I30QPnU9vVPNGlTGzOHGJ56f03VSTd3MHb5nuPu3pA9rut9Lnh6HzfMTbJpL9W/3AOjn9x+6rAkkIBhezpGt+7o4LRF28dfOCVOb6W9UpCd0NbanxtjzgNOAdqAdP6mFOFItg7YCqy21voAxph7CU8G8wPgceCHBwo2bzowxxgzCJhL+N//EmPM5621vzjw0l0y3N5lktUwa233+w7yjDHD8qPeXonTfxkqWSk7lz1pxo5tnn3v9lFzkzY5dIDB5ozNWkM2l7BZjMnmErmcTZC1CZu1CToxNptLkiW8niVhc4TXO03SZkmSI5z3kSXcwHPg+ivfG8hZS85gLZAz1loDFksuvAyvm1cuw58lwBrAWGzCWgCbJP+Ga7v9DcP34hzYdsi2h3d75Q3aHnadbq7bY3g/f3WWVz/HttZHzp40aszmW1bMXtVunvUXbWkdcPOKs57rep/ducUn7Ko+bsNsb/i+n7346BkPrhrQUl1tE7ta22rW7ElmO7K5xIcfqmn5+EVnvPoUhhYOy97T73qEv4c57O9x9Oc4/DFH+Bsf9oC+/Y1Nymz9WJ8ecVSDCUesbcAuwg+H+/OXKeC7wLuNMQGwATiJcFPtp4EPArOMMb87sHkYOA/4I/BVa+3HgfOMMX8g3DR86O9hTDUw1Fq71RiTzj//0YwCHjLGXG2tfao3v1ycSjbrOoBIX9V05qaf98Li6bCYvQPHr9o4bt7abaNOrmuvHnIixhxx+cUDlXkIQ4HGF30TzuTMZcMv24nN5SDXCbkc2E4bXg/vY3MH7hv+zOZy9pXvszZ/m8XmbPjzrM0/xoZVncvZ8MODJfzgkAt/Hv4s/OCQvy/hhwnCDxV0+VAB5BKpZCrX1EJNS+eY5N79thqSpqVzRHU4oA8/XGxr3l//9Oqm8Xv31+zryNrUkAEDEzPHjdn3+yWZuiF1A7Mbd+2u3rKntap1f9UUwpF1EmyC8L31wOWBzZfxeb/tsEsK8TTGmCuAc4BlhCPQKsIJT/sIN90OAL5J+LebSPcl+AKHDrIWAm8HPm+MuQQYCDxurd132OOqgDsJC/lHhPuD7+oh8jbgLcA9xpj3W2v/0tPvGJ9/dI1kpczV79s45YTlv5pywvJfkTOp9u0jZj+3cfy85l1Dpo/PJatL9rjacFNbMgHJcCbmYUVfqge3jB/xPGt3rBx135KWUdub25gyZhYPL0uceekZH3jlPvNmjzTqN0EAAByFSURBVOeRF3458OmVixgxaCInzbhySPr4s4esavo+w+vHVK3fdQ+JRB21wz55+AzYbnX5QNIJNnuEDyTZAx9AunwgyR784NHjBxLyj7e9/EBiunyI6fYDSTgezpnww0fOYAYcdZNqHywC/h/wIeAZwqKtBhYDxxN+ODmNcJ/pN6y1r8tvLh4D3JR/7IWEs4oxxlwJZKy1G40xnwAeyj/fa7p57Z8BDdbam40xFwOXAM3GmNutPfJ5n621y4wxbwHmASpZkXKUsJ01o7cvPnX09sUAtNUM3bxp7NnLt4yZm2gZMMbDmOGOI5a9U6ddwOadq9m6az2JRIoZE05mf0fbIfc5ZepruOfJW/EmzmXzrrWkjzsLgCEDR3LCxFN58uXfM3bYcd09fbfK9QNJN57s+S49s9auNOGW7ay19lxjzIPATsLZwNcQjnC3E876PcEYs5uwNJPAjwlHoDuBwBhzNvAvwPn5WcsfBpoIR593GmP+B3jYWtsOTCU8bOguY8yNhEV9FuHhQ/cbY17bzci3a+6lwKt3EXRDJStSBmrbd42dsubBsVPWPIjF5HYNnb50w7hzt+4cPmt4Z2rAifTieD051Nzp83n8xd+AgfrawZw4+UyeX/HYIfdZvPJxEokkW3ato66mnqXrnmHW5DOYOfFUjhs1k/raIczz3+ToN3Bqf8936bV/Bd5vjDmTcH/rOwn3qw4HbgDeBHwJuNNa+7Qx5suAB/zWWnubMaYWOIFw0/I7gY8CU4DbrLWfBjDGzCUcDe8F/kRYpnsJZzD/CrgkX753G2NG5x9/+PriNRzD8cHG2u6nK5SbwPNPJdz0IFJROpIDdm8ZMzfYNPasjr31k6bYRLJXmy4FWtqbyaxfxPRxcxhcp40DffCVa2+Z//lCPJExZhjw39baNxtjHuXggD6VH93+Gni/tXZXl8d8Gthkrf1ZP187EfXqUBrJipS5qmzrkIkbnzhr4sYnANg7cPzKDePmrds26uSB+6uHzDraBKpKV1cziFOnXeA6Rjkq5Ej2la0w1toLu95gjLkKOA44fPGLesLNxv1SjOUX4zSSnQUUZMabSFzkTKp9+8j0ko3jzin5CVRSVj597S3zvxn1ixhjktbasj5yJE4j2Q7XAURKTcJ21oze9vypo7c9D0BbzbBNm8aevWLzmLmJ1gGjfcJNdSJ91VKMFyn3goV4lWyvV+AQqVS17U3jpqx5YNyUNQ8cmEC1ZMO4c7fnJ1DN0gQq6aUjHuIih4pTye4kPKgrTmcWEomMwSaG7Xr5xGG7wrXTO1IDdm8ZfXp+AtXEqTaRnOA4Yklr2ruVYfWjXcdwZbvrAOUiNoXkZ4IcYdGKyDGo6mwdMnHj42ed/ty/n3fh4x+bcPozX1s5YcNjj1W373qWLufzjJM9LTv5+l0fOurt373n469cv+epn/DD+z+DtZaXNi4uRsRSpZLtpTiNZCH8hx/pOoRIHAzat2HqzJd/OXXmy78km0i1bR8xZ9HGcfP27h4ydUIuWT3ddb5CuPvJW+nItnd7W0t7Mz979N9p7zy4QMXetl1MGjmD9dtfruRRLKhkey1uJbuN8CBlESmgZK6zdsy2504bsy1cP7+1ZvimTePOWr5l9Omp1gGjvHKcQLVsw/NUp2oZNKD742ONSXD16z7HrQ91ORzUWnK5LMs3NXJB+i1FSlqSVLK9FJvNxXnbXAcQqQQD2neOm7r6gfPOfvpLZ1/42HVDTl5805LRWxf9KdXZ0kgZzAjtzHbwu+d+xuVnXnPE+wyoHsiAmvpDfjZu+BR27t2CMYbv/fYTbG5aE3XUUrTv2lvmt/V8N4H4jWT16UqkyAw2MXzXshOH71oGQEeqbld+BarO5vqJ0zDJ8Y4jvsrDi+/kvFmXU3dYifZk/py30bj6rzS3NnHSlPN4ce1TfVq7OCY0s7gP4layGsmKOFbV2TJ04obHz5644XEAmusnrtg4bt76bSNPGri/evBswrVmncqsf45lGxbz+JJ72LBjObc/9i2uOv/6Xj22df8+aqrq6Mx10t4Ry/lgPdFgpg/iVrL6xxcpMYP2rp828+VfTJv58i/yE6hOWrRx3Dl7dw+ZNjGXrJrmItMnL//eK99/77f/yPw5b+Pep3/KpWdcfdTHbdm1jgkjplJTVcfND/4L777g01FHLUV6n+2D2CyrCBB4/lXAba5ziEjvhBOozl6+ZfTcVOuAUT7GDHWdSXp0x7W3zL/KdYhyEbeRrDYXi5SRcALV/eOmrr4fi8k2DT3hxY3j523fMXzWyGyy1tcKVCVpi+sA5SRuJbvWdQAROTYGmxy+a9nsrhOoNo85Pdg89qzO5voJ0zHJcY4jSmi16wDlJG4luwLI0uXUSSJSnqo6W4ZO2vDY2ZM2hCdSb66fuGLDuHPXbx85p35/9eATS2ECVYVa6TpAOYnVPlmAwPNXAFNd5xCR6GQTVa3bR85ZsnHcvH27h0ydmEu4mUBVoWZfe8t8nVa0l+I2kgV4GZWsSKwlcx0DxmxdNHfM1kUAtNYO37hp7DkrtoyZm2qtHTkLY4Y4jhhnBRnJHjhXrDHGEG59zB04iXr+Z6YYJ1WPWhxHsjcB17nOISJuWEy2adjMpRvGnbtz53DvwASquK1u58rma2+Z3+9948aYMcDPgNOAZ/NfpwKTgE7CSazfstY+1OUxnwN2WWt/2N/XL6a4jmRFpEIZbHJ4UyY9vCkDQEdqYNPmMadnNo09q3Nv/YTpmIQmUB27gry/Wmu3GGMuA+6z1i4AMOHhW58D/gj81Vq767CHtREWcFlRyYpIrFV17hs2acOfzp604U8A7KmftHzj+HM3bB85p35/1SBNoOqbZQV8rjcADwMYY1LAC8ATwDuBm40xx9sYbGqNY8m+5DqAiJSuwXvXTR/80p3TeelOsomq1m0jT3p247hz9u3RBKreyBTiSfKleiOQNMZ8BJgHPAN8n/AIkRRwhTHms8D+/MPGAVljzHvz12uBr1hrf12ITFGJY8muATqAKtdBRKS0JXMdA8ZufXbu2K3PAtBaO2LDxnHnrNw6+rSq1tqRviZQvUqhRrJXE5bnF4A3cbBITz3wvbX2l8AvDzzAGHM9sNdae0uBMhRF7ErWzwTZwPNXAjNdZxGR8jKgbceEaavunTBt1b1YTHbnMK9x4/h5O3YO80ZpAhVQoJEs0MDBYgUw+cvJwH3AJQV6HediV7J5L6GSFZF+MNjkiKYgPaIpAGB/1cCdm8eckdk85sxcfgLVWMcRi60dWFWIJ7LWtodH6fDvwAjg3wgL/ALgb0DrgUN8CvF6LsW1ZBuBS12HEJH4qO7YN3zy+kfPmbz+UQD21E96eeP4czduGzlnUEc4garGccSoLb72lvmFLL19wEeAWUA9MB24g3AW8TnANGPMaiB7eNmacE3rhLW2o4B5IhG742QBAs+/HPiN6xwiUhmyieqWbaNOWrpx7Dn7dg+ZMskmquK4IM4Prr1l/seieGJjzAeAVmvt7fnr5wHrgA8BrwUOX5QiAfzSWvutKPIUUlxLdiywyXUOEalMrbUj128cd86qLaNPq2qrHRGXCVTvufaW+T9zHaLcxLJkAQLPX0u4eoiIiDM5k+hsGjpz6Ybx5zY1DfNGZZM1XplOoPKuvWV+IY+TrQhx3ScL8BQqWRFxLGFzqRFNwZxDJ1Cdmdk89szc3oHjZ2ASYxxH7I3daA2CYxLnkez1wDdd5xAROZo9gya/vGHcuRu3j5wzqKOqvlQnUD1y7S3zX+c6RDmK80j2adcBRER6Mrh57YzBzXfM4KU7yCaqW7aOOvmZTePOadk9+PjJNlE1xXW+PL2fHqM4l+widAJ3ESkjydz+unFbnj593Jaw01oGjFy/cdy8lVtHnVrdVjtiFsYMdhRNJXuMYru5GCDw/BeAOa5ziIj0V84kOpuGeUs3jJu3s2mYN7rIE6gmXHvL/I1Feq1YifNIFsLJTypZESl7CZtLjdi5dM6InUsB2F9Vv2PzmDOXbR57RtQTqDaqYI9dJZTsNa5DiIgUWnXH3hGT1z9yzuT1jwCwZ9BxL20YP2/j9hFzhuQnUFUX6KWeLNDzVKS4l+yfXQcQESmGwc1rThi8bM0JcAfZRPW+raNOeWHTuLNbdg+eMtkmUv2ZQPXHgoWsQLHeJwtalEJEJD+BatXWUadVt9UO9/s4gUqLUPRDJZTsfxGeu1BEpOLlTKJz5zB/ycbx85qahs4ck59AZY5w93XX3jJ/clEDxkzcNxcD/B6VrIgIEE6gGrlzyUkjdy4BYH/VoO2bxp65bPOYM+y+geNOwCRGd7n7I25SxkcllOwjgOXgSYFFRCSvuqN55HHr/jDyuHV/wILdM+j4ZRvHz9u0fUR6aDZZ83vX+cpd7DcXAwSe/yxwmuscIiJlxAJj/EywzXWQclaOZ4I4Fg+4DiAiUmaeUcH2X6WU7P2uA4iIlBkNTgqgUkr2GUCfyEREek8lWwAVUbJ+JsgBD7rOISJSJrYCz7oOEQcVUbJ52mQsItI79/uZIP6zYougkkr2AaDVdQgRkTLwc9cB4qJiStbPBHuBe13nEBEpcVvQIhQFUzElm3e76wAiIiXuV34myLoOEReVVrIPAjtdhxARKWF3uA4QJxVVsn4m6ADucp1DRKRErfIzwf+5DhEnFVWyedpkLCLSPU14KrBKLNkngHWuQ4iIlCBtKi6wiivZ/LFfd7rOISJSYl70M8GLrkPETcWVbJ42GYuIHEqj2AhUZMn6meBvgD6xiYgcpC18EajIks3TaFZEJPSonwlWuw4RR5Vcsj8F2l2HEBEpAT9wHSCuKrZk/UywFfil6xwiIo6tBX7rOkRcVWzJ5t3kOoCIiGM3axnF6FR0yfqZ4FlAq5uISKVqA37iOkScVXTJ5mk0KyKV6ud+JtjuOkScqWTh18BG1yFERBzQhKeIVXzJ5k8acIvrHJVsU0eH6wgilej//EzwnOsQcVfxJZt3Kzqcp9e+vGUzj+5tPuL17mzv7ORda9e8cv0727Zyzbp1WGt5uqUlsqwickQ/dB2gEqhkeeVwnl+4zlEOnm1pYXtnJxfWD+r2end2Z7P8y6ZNtOZyr/ysKZvFr60haG9nXFUq8twicojNwK9ch6gEKtmDtG+iBx3W8oUtm5lQVcUjzc2vun4kSeDb48dTnzj4n5sFsjYs6dMH1EUfXkS6ujm/q0wippLNyx/O84jrHKXst7t3M626mquHj6CxrY2f72o65PptTTu7fVx9MsmgZPKQn82ormFjZwfGwLvXrWVFu7bWixTJLuD7rkNUCpXsob7gOkApC9rbuGLoUEalUlw6eDB/3Lv3kOt92be6cPhwLhs8mAEmwUX1g3hs394Ik4tIF9/1M8Fu1yEqhUq2Cz8T/AX4vescpWpyVTXr94dbmJa0tfFUS8sh18dXVfXp+ZqzOQYmElQbg7UFjysir9YEfM91iEqikn21z7sOUKreOnQIT7W08O61a7hzVxMPT512yPX3DRvO8vZ2vr9tW4/PtXr/fmbW1JCureX2XU3MrdN+WZEi+I6fCfa4DlFJjNUQ4lUCz78feIPrHCIiBbQTON7PBEc/3k4KSiPZ7mk0KyJx820VbPGpZLvhZ4JF6NRPIhIfO9Bhik6oZI/sC4SHc4qIlDuNYh1RyR6BnwkWA3e7ziEi0k/b0SjWGZXs0Wk0KyLl7t/9TKAD0R1RyR6FnwleBP7XdQ4RkWO0HK3u5JRKtmefAXRcmYiUo0/4mWC/6xCVTCXbAz8TbAG+6DqHiEgfPeBngvtdh6h0Ktne+QGw1HUIEZFe2g98wnUIUcn2ip8JOoHrXOcQEeml7/mZ4GXXIUQl22t+JvgjcJfrHCIiPdgEfMV1CAmpZPvmH4Hen89NRKT4PqNDdkqHSrYP/EywDvg31zlERI7gr8BtrkPIQSrZvvsmsMJ1CBGRw+SAj/mZQAvolBCVbB/5maAdzdoTkdLzH/mTm0gJUckeAz8T3Afc7jqHiEjecsKFc6TEqGSP3T8AG1yHEJGKlwPe62cCTcosQSrZY+Rngl3AB1znEJGK910/E/zFdQjpnkq2H/xM8DvgVtc5RKRiBcDnXIeQI1PJ9t/1wErXIUSk4nQCC/1M0OY6iByZSraf8gd9LyTcLyIiUixf9zPBM65DyNGpZAvAzwR/Br7rOoeIVIwXgC+7DiE9U8kWzg3oTD0iEr39wHv8TNDhOoj0TCVbIPlFKt5DuJ9ERCQqX/Azwd9ch5DeUckWUH61lX92nUNEYus+4BuuQ0jvGWu1zGWhBZ5/F/BW1zlEJFZWAqflj9GXMqGRbDTeB2RchxCR2GgD3qqCLT8q2Qj4maAZ+Dug2XUWEYmFj/qZYLHrENJ3KtmI+JkgA1ztOoeIlL3/9DPBf7sOIcdGJRshPxPcBXzLdQ4RKVvPAte5DiHHTiUbvc8Cj7oOISJlZwfwtvzhgVKmVLIR8zNBFrgSWO86i4iUjRxwlZ8J1rgOIv2jki0CPxNsBd5GuFKLiEhPbvQzwUOuQ0j/qWSLxM8ETwHvB3RgsogczX/6meBrrkNIYahki8jPBLcR7qMVEenO/cBHXIeQwtGKTw4Env994GOuc4hISXkGuNDPBPtcB5HC0UjWjU8Cv3IdQkRKxgrgjSrY+NFI1pHA82uAh4DzXWcREae2Aef4mWC56yBSeCpZhwLPHwo8Acx2nUVEnGgh3ET8tOsgEg1tLnYov9j3xcA611lEpOiywJUq2HhTyTrmZ4INwCVAk+ssIlJU1/qZ4F7XISRaKtkS4GeCJcBlgCY9iFSGT/uZ4FbXISR6KtkS4WeCPwNvREUrEnf/7GeCb7oOIcWhki0hfiZ4DHgT4WQIEYmfz/mZ4OuuQ0jxqGRLjJ8J/oSKViSOvuhngn91HUKKSyVbgvxM8Chh0WrTsUg83Ohngi+5DiHFp+NkS1jg+fOAB4DBrrOIyDG73s8E33YdQtxQyZa4wPPnEq4MNdx1FhHpEwtc52eCH7kOIu6oZMtA4PlzgIeB0a6ziEiv5IAP+5ngP10HEbdUsmUi8PyZwO+A4x1HEZGjawWu8jPB3a6DiHsq2TISeP5o4F7gDNdZRKRb24BL/UzwlOsgUho0u7iM+JlgK3AB8P8cRxGRV1sGnKWCla5UsmXGzwStwBWAZiuKlI4nCE9Xt9J1ECkt2lxcxgLP/wjwAyDpOotIBbsTeJ+fCdpdB5HSo5FsHxhjrjPGfOCwn1UbY+YaYz5qjLnfGHOuMaYmf9s3jTGvjSqPnwluJjyxwN6oXkNEjurfCCc5qWClWynXAUqdMSZlre3MX+0AOrveBtQRbr49B7jWWvs3Y8wPjDF/JDxfZDbKfH4meCDw/HOB+4EJUb6WiLyiE/ioDtGRnmhz8VEYY4YDdxOWay0wL3/TY4T/k1UB7wS2ALcAXyU8AfsAwtJbDNxtrf1T1FkDz59AOPP4lKhfS6TCbQfe6WeCh10HkdKnkexRWGt3AucDGGOuBQzhJvZbrbV35n8+H/glMAuYDdxBeCD6fKBoi4H7mWBD4PnnAN8HPlis1xWpMH8F3u5ngvWug0h50Ei2F4wxIwgPm/lV/kd/D1xmrd2Vv30IsB74IWHJXkm4OtMO4HfFGMl2FXj+VcCtwMBivq5IzH0H+KyfCTpcB5HyoZLtQX4S028IJzjMAtqAncAngSustduNMTcAVwPvAC4Hvk6+iAlHvg9Ya28uZu7A8z3CDwWzi/m6IjG0m3D2sFZwkj5TyR6FMWYq8L/Anwg3Gw8n3BS8G/gDsBB4G/B5wpVevgpcB0wEFgFnAz+11t5b7OwAgefXAT8C3uvi9UVi4DngCh3/KsdKJXsUxpjjgdOttb/KX78WaLPW/lf++kzCdUoTwOeAr1prV3d5/NdxsLn4cIHnv5ewbOtc5hApM7cCH9fhOdIfmvh0FPnCXN3lR0mgusvtyw58b4ypJpxt3FUVUBNdwt7xM8H/BJ7/DOHmY991HpEStw/4kJ8JbncdRMqfRrIVJPD8gYQrRL3PdRaREvV/hPtfl/V4T5FeUMlWoMDzLyHcFDbJdRaREtEK3Ah8188EOddhJD5UshUq8PzBwDfRMbUifwGu9jPBS66DSPyoZCtc4PmvBf4TmOI6i0iRtQA3ADdp9CpRUcnKgX21XwcOrGolEndPEI5el7sOIvGmkpVXBJ5/HvBTYLrrLCIR2Qf8M/BDPxPozU8ip5KVQwSePwD4CvBxdIiXxMv9wMe0sIQUk0pWuhV4vg98G7jEdRaRfgqAT/qZ4CHXQaTyqGTlqALPX0C4MPos11lE+mgn8EXgZj8TdPZwX5FIqGSlR4HnJ4EPAV8CRjqOI9KTTsLzO3/BzwQ7XYeRyqaSlV4LPH8o4ckQ/oFXLyEpUgp+T7hpeKnrICKgkpVjEHj+DMKFLC53nUUk7yXgU34muM91EJGuVLJyzALPvwD4AnCB2yRSwZYDXwN+pv2uUopUstJvgeefS7ju6+tdZ5GKkQH+FbjTzwRZ12FEjkQlKwUTeP4ZhOfVvdR1FomtF4GvAr/SUohSDlSyUnCB559EWLZvITyhvUh/LSZcJOVurdQk5UQlK5HJL2hxA3Al4QnvRfrqWeArfib4resgIsdCJSuRCzx/GvARwpPFD3ccR0rffuAu4D/8TPAX12FE+kMlK0UTeH4tcAVh4Z7tOI6UntXArcB/+Zlgm+MsIgWhkhUnAs+fQ1i2VwGDHMcRd3LA74D/AB7UZCaJG5WsOBV4/iDCov0wcJLjOFI824H/Am71M8Eq12FEoqKSlZIReP7ZhPtt/w6tkRxHbcADwC+Ae/xM0O44j0jkVLJScgLPTwEXAn9PWLgj3CaSfthPuDn4l8Bv/UzQ7DiPSFGpZKWk5Qt3PgcLV7OTS18H8AfCEetv/Eyw23EeEWdUslI28oX7WsIZyirc0tIB/ImwWO/WKeZEQipZKUv5wj2HcL3kBcCpaHWpYlsFPES4OfiP2hQs8moqWYmFwPNHABcBryPcnzvVbaJY2gk8BvwR+L2fCV5ynEek5KlkJZYCz59MWLYX5C+PcxqoPO0AniQs1UeBF3Qcq0jfqGSlIgSeP5pwk3LXrylOQ5WW7cCirl9+JljjNpJI+VPJSsUKPH8YcAqHFu8M4r9vdxuvLtS1biOJxJNKVqSLwPPrCYt2Spev47tc1rnK1gdZYB2wssvXqgPf+5lgu8NsIhVFJSvSB4Hnj+HQ4p0ADD3C18ACvvR+oOkoX+s5WKhr/UzQWcDXFpFjpJIViUjg+VUcWrr1gAEO/E9nD/u+62UO2EO+RP1MsK8YmUWksFSyIiIiEYn7BA8RERFnVLIiIiIRUcmKiIhERCUrIiISEZWsiIhIRFSyIiIiEVHJioiIREQlKyIiEhGVrIiISERUsiIiIhFRyYqIiEREJSsiIhIRlayIiEhEVLIiIiIRUcmKiIhERCUrIiISEZWsiIhIRFSyIiIiEVHJioiIREQlKyIiEhGVrIiISERUsiIiIhFRyYqIiEREJSsiIhIRlayIiEhEVLIiIiIRUcmKiIhERCUrIiISEZWsiIhIRFSyIiIiEVHJioiIREQlKyIiEhGVrIiISERUsiIiIhFRyYqIiEREJSsiIhIRlayIiEhEVLIiIiIRUcmKiIhERCUrIiISEZWsiIhIRFSyIiIiEVHJioiIREQlKyIiEhGVrIiISERUsiIiIhFRyYqIiEREJSsiIhIRlayIiEhE/j/9yLBRorjEqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(edu_counts.values, labels=edu_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >edu</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow0_col1\" class=\"data row0 col1\" >31687</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow0_col2\" class=\"data row0 col2\" >14814</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow0_col4\" class=\"data row0 col4\" >54933</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow0_col5\" class=\"data row0 col5\" >268</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.26%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow1_col1\" class=\"data row1 col1\" >21311</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow1_col2\" class=\"data row1 col2\" >4000</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow1_col5\" class=\"data row1 col5\" >4233</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow1_col6\" class=\"data row1 col6\" >4.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow2_col1\" class=\"data row2 col1\" >17161</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow2_col2\" class=\"data row2 col2\" >5250</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow2_col5\" class=\"data row2 col5\" >68184</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow2_col6\" class=\"data row2 col6\" >66.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow3_col1\" class=\"data row3 col1\" >13632</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow3_col2\" class=\"data row3 col2\" >4500</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow3_col4\" class=\"data row3 col4\" >27500</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow3_col5\" class=\"data row3 col5\" >30246</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow3_col6\" class=\"data row3 col6\" >29.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow4_col0\" class=\"data row4 col0\" >初中及以下</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow4_col1\" class=\"data row4 col1\" >13200</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow4_col2\" class=\"data row4 col2\" >9000</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow4_col3\" class=\"data row4 col3\" >11100</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow4_col5\" class=\"data row4 col5\" >15</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow5_col0\" class=\"data row5 col0\" >高中</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow5_col1\" class=\"data row5 col1\" >8970</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow5_col2\" class=\"data row5 col2\" >4622</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow5_col3\" class=\"data row5 col3\" >7000</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow5_col4\" class=\"data row5 col4\" >23550</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow5_col5\" class=\"data row5 col5\" >219</td>\n",
       "                        <td id=\"T_4b6d9068_2f5c_11ea_895a_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.21%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2560f3eb088>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['初中及以下','高中','大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是13481，17417，21798，32183。中位数分别是12500， 15500，20000， 30000。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=99.82075932370368, pvalue=8.531566536982412e-23)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=368.64021013143685, pvalue=1.5732344246209446e-81)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1790.6486660491666, pvalue=0.0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >experience</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow0_col0\" class=\"data row0 col0\" >10+</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow0_col1\" class=\"data row0 col1\" >31526</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow0_col2\" class=\"data row0 col2\" >12391</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow0_col4\" class=\"data row0 col4\" >58700</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow0_col5\" class=\"data row0 col5\" >416</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow1_col1\" class=\"data row1 col1\" >22010</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow1_col2\" class=\"data row1 col2\" >11500</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow1_col5\" class=\"data row1 col5\" >16535</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow1_col6\" class=\"data row1 col6\" >13.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow2_col1\" class=\"data row2 col1\" >17429</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow2_col2\" class=\"data row2 col2\" >9000</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow2_col5\" class=\"data row2 col5\" >37186</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow2_col6\" class=\"data row2 col6\" >30.78%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow3_col0\" class=\"data row3 col0\" >no</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow3_col1\" class=\"data row3 col1\" >14687</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow3_col2\" class=\"data row3 col2\" >3500</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow3_col4\" class=\"data row3 col4\" >37500</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow3_col5\" class=\"data row3 col5\" >34820</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow3_col6\" class=\"data row3 col6\" >28.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow4_col1\" class=\"data row4 col1\" >13527</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow4_col2\" class=\"data row4 col2\" >5500</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow4_col5\" class=\"data row4 col5\" >31870</td>\n",
       "                        <td id=\"T_4ba42fe8_2f5c_11ea_922d_701ce71031efrow4_col6\" class=\"data row4 col6\" >26.38%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x256102e1588>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_type</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow0_col0\" class=\"data row0 col0\" >外资（欧美）</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow0_col1\" class=\"data row0 col1\" >19689</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow0_col2\" class=\"data row0 col2\" >6000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow0_col4\" class=\"data row0 col4\" >45000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow0_col5\" class=\"data row0 col5\" >6593</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow0_col6\" class=\"data row0 col6\" >5.46%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow1_col0\" class=\"data row1 col0\" >合资</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow1_col1\" class=\"data row1 col1\" >16436</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow1_col2\" class=\"data row1 col2\" >6000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow1_col3\" class=\"data row1 col3\" >15000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow1_col4\" class=\"data row1 col4\" >35144</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow1_col5\" class=\"data row1 col5\" >10751</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow1_col6\" class=\"data row1 col6\" >8.90%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow2_col0\" class=\"data row2 col0\" >民营公司</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow2_col1\" class=\"data row2 col1\" >16098</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow2_col2\" class=\"data row2 col2\" >5000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow2_col3\" class=\"data row2 col3\" >14000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow2_col5\" class=\"data row2 col5\" >88217</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow2_col6\" class=\"data row2 col6\" >73.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow3_col0\" class=\"data row3 col0\" >国企</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow3_col1\" class=\"data row3 col1\" >15894</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow3_col2\" class=\"data row3 col2\" >3500</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow3_col3\" class=\"data row3 col3\" >14000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow3_col4\" class=\"data row3 col4\" >45000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow3_col5\" class=\"data row3 col5\" >7631</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow3_col6\" class=\"data row3 col6\" >6.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow4_col0\" class=\"data row4 col0\" >外资（非欧美）</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow4_col1\" class=\"data row4 col1\" >15860</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow4_col2\" class=\"data row4 col2\" >5250</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow4_col3\" class=\"data row4 col3\" >14000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow4_col4\" class=\"data row4 col4\" >36563</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow4_col5\" class=\"data row4 col5\" >6955</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow4_col6\" class=\"data row4 col6\" >5.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow5_col0\" class=\"data row5 col0\" >非营利组织</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow5_col1\" class=\"data row5 col1\" >14499</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow5_col2\" class=\"data row5 col2\" >6288</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow5_col3\" class=\"data row5 col3\" >12500</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow5_col4\" class=\"data row5 col4\" >29124</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow5_col5\" class=\"data row5 col5\" >103</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow6_col0\" class=\"data row6 col0\" >事业单位</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow6_col1\" class=\"data row6 col1\" >13899</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow6_col2\" class=\"data row6 col2\" >5250</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow6_col3\" class=\"data row6 col3\" >12500</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow6_col4\" class=\"data row6 col4\" >30725</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow6_col5\" class=\"data row6 col5\" >531</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow6_col6\" class=\"data row6 col6\" >0.44%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow7_col0\" class=\"data row7 col0\" >外企代表处</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow7_col1\" class=\"data row7 col1\" >13043</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow7_col2\" class=\"data row7 col2\" >7000</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow7_col3\" class=\"data row7 col3\" >11667</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow7_col4\" class=\"data row7 col4\" >22133</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow7_col5\" class=\"data row7 col5\" >46</td>\n",
       "                        <td id=\"T_4bda5c36_2f5c_11ea_9f77_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.04%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25610356b88>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_size</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000+</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow0_col1\" class=\"data row0 col1\" >24086</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow0_col3\" class=\"data row0 col3\" >22500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow0_col4\" class=\"data row0 col4\" >45000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow0_col5\" class=\"data row0 col5\" >7704</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow0_col6\" class=\"data row0 col6\" >6.38%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow1_col0\" class=\"data row1 col0\" ></td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow1_col1\" class=\"data row1 col1\" >21587</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow1_col2\" class=\"data row1 col2\" >7000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow1_col4\" class=\"data row1 col4\" >58333</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow1_col5\" class=\"data row1 col5\" >944</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow1_col6\" class=\"data row1 col6\" >0.78%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow2_col0\" class=\"data row2 col0\" >5000-10000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow2_col1\" class=\"data row2 col1\" >19490</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow2_col4\" class=\"data row2 col4\" >45000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow2_col5\" class=\"data row2 col5\" >3291</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow2_col6\" class=\"data row2 col6\" >2.72%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow3_col0\" class=\"data row3 col0\" >500-1000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow3_col1\" class=\"data row3 col1\" >16639</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow3_col2\" class=\"data row3 col2\" >3750</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow3_col4\" class=\"data row3 col4\" >35000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow3_col5\" class=\"data row3 col5\" >11838</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow3_col6\" class=\"data row3 col6\" >9.80%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow4_col0\" class=\"data row4 col0\" >1000-5000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow4_col1\" class=\"data row4 col1\" >16599</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow4_col2\" class=\"data row4 col2\" >5500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow4_col4\" class=\"data row4 col4\" >35000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow4_col5\" class=\"data row4 col5\" >15922</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow4_col6\" class=\"data row4 col6\" >13.18%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow5_col0\" class=\"data row5 col0\" >150-500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow5_col1\" class=\"data row5 col1\" >15950</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow5_col2\" class=\"data row5 col2\" >5250</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow5_col3\" class=\"data row5 col3\" >14583</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow5_col4\" class=\"data row5 col4\" >35000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow5_col5\" class=\"data row5 col5\" >27593</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow5_col6\" class=\"data row5 col6\" >22.84%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow6_col0\" class=\"data row6 col0\" >50-150</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow6_col1\" class=\"data row6 col1\" >15260</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow6_col2\" class=\"data row6 col2\" >4500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow6_col3\" class=\"data row6 col3\" >14000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow6_col4\" class=\"data row6 col4\" >35000</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow6_col5\" class=\"data row6 col5\" >36693</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow6_col6\" class=\"data row6 col6\" >30.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow7_col0\" class=\"data row7 col0\" >50-</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow7_col1\" class=\"data row7 col1\" >14030</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow7_col2\" class=\"data row7 col2\" >4500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow7_col3\" class=\"data row7 col3\" >12500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow7_col4\" class=\"data row7 col4\" >32500</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow7_col5\" class=\"data row7 col5\" >16842</td>\n",
       "                        <td id=\"T_4bead6ee_2f5c_11ea_b3fa_701ce71031efrow7_col6\" class=\"data row7 col6\" >13.94%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2561035c348>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_4c000b62_2f5c_11ea_927a_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >industry</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow0_col0\" class=\"data row0 col0\" >finance</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow0_col1\" class=\"data row0 col1\" >18916</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow0_col2\" class=\"data row0 col2\" >5500</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow0_col4\" class=\"data row0 col4\" >58333</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow0_col5\" class=\"data row0 col5\" >4050</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow0_col6\" class=\"data row0 col6\" >3.35%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow1_col0\" class=\"data row1 col0\" >logistic</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow1_col1\" class=\"data row1 col1\" >18590</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow1_col2\" class=\"data row1 col2\" >6000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow1_col3\" class=\"data row1 col3\" >15499</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow1_col4\" class=\"data row1 col4\" >43225</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow1_col5\" class=\"data row1 col5\" >1851</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow1_col6\" class=\"data row1 col6\" >1.53%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow2_col0\" class=\"data row2 col0\" >edu</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow2_col1\" class=\"data row2 col1\" >16586</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow2_col2\" class=\"data row2 col2\" >5250</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow2_col4\" class=\"data row2 col4\" >41666</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow2_col5\" class=\"data row2 col5\" >7001</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow2_col6\" class=\"data row2 col6\" >5.79%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow3_col0\" class=\"data row3 col0\" >computer</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow3_col1\" class=\"data row3 col1\" >16282</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow3_col2\" class=\"data row3 col2\" >5000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow3_col3\" class=\"data row3 col3\" >14583</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow3_col4\" class=\"data row3 col4\" >37500</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow3_col5\" class=\"data row3 col5\" >92345</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow3_col6\" class=\"data row3 col6\" >76.43%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow4_col0\" class=\"data row4 col0\" >trade</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow4_col1\" class=\"data row4 col1\" >15409</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow4_col2\" class=\"data row4 col2\" >5250</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow4_col3\" class=\"data row4 col3\" >14000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow4_col4\" class=\"data row4 col4\" >33333</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow4_col5\" class=\"data row4 col5\" >6836</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow4_col6\" class=\"data row4 col6\" >5.66%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow5_col0\" class=\"data row5 col0\" >service</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow5_col1\" class=\"data row5 col1\" >15408</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow5_col2\" class=\"data row5 col2\" >6500</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow5_col4\" class=\"data row5 col4\" >30000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow5_col5\" class=\"data row5 col5\" >687</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow6_col0\" class=\"data row6 col0\" >gov</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow6_col1\" class=\"data row6 col1\" >15205</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow6_col2\" class=\"data row6 col2\" >3082</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow6_col3\" class=\"data row6 col3\" >13000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow6_col4\" class=\"data row6 col4\" >34508</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow6_col5\" class=\"data row6 col5\" >1359</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow6_col6\" class=\"data row6 col6\" >1.12%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow7_col0\" class=\"data row7 col0\" >ads</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow7_col1\" class=\"data row7 col1\" >15174</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow7_col2\" class=\"data row7 col2\" >5815</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow7_col3\" class=\"data row7 col3\" >12916</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow7_col4\" class=\"data row7 col4\" >35000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow7_col5\" class=\"data row7 col5\" >1314</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow7_col6\" class=\"data row7 col6\" >1.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow8_col0\" class=\"data row8 col0\" >energy</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow8_col1\" class=\"data row8 col1\" >15135</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow8_col2\" class=\"data row8 col2\" >5250</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow8_col3\" class=\"data row8 col3\" >15000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow8_col4\" class=\"data row8 col4\" >30000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow8_col5\" class=\"data row8 col5\" >1066</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow8_col6\" class=\"data row8 col6\" >0.88%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow9_col0\" class=\"data row9 col0\" >medical</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow9_col1\" class=\"data row9 col1\" >14970</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow9_col2\" class=\"data row9 col2\" >4731</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow9_col3\" class=\"data row9 col3\" >14000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow9_col4\" class=\"data row9 col4\" >30000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow9_col5\" class=\"data row9 col5\" >2574</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow9_col6\" class=\"data row9 col6\" >2.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow10_col0\" class=\"data row10 col0\" >realestate</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow10_col1\" class=\"data row10 col1\" >14775</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow10_col2\" class=\"data row10 col2\" >3750</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow10_col3\" class=\"data row10 col3\" >13000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow10_col4\" class=\"data row10 col4\" >35000</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow10_col5\" class=\"data row10 col5\" >1744</td>\n",
       "                        <td id=\"T_4c000b62_2f5c_11ea_927a_701ce71031efrow10_col6\" class=\"data row10 col6\" >1.44%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25610315f88>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
